With the advent of computers and digital typography, the number of different fonts has continued to grow. As a result, users often have wide flexibility in choosing fonts in various applications. Given the large number of available fonts, the task of identifying similar fonts has become more challenging. In particular, there are a number of scenarios in which it may be desirable to identify the similarity between two fonts or among a collection of fonts. For instance, given a picture containing text, a user may wish to find a font that is similar to the font in the image. As another example, a collection of fonts may be organized by visual similarity to facilitate a user selecting a font from the collection (as opposed to, for instance, simply listing fonts alphabetically). Furthermore, in instances in which a particular font is not available in an application, a user may wish to select another font that is visually similar to the unavailable font.
Some systems, such as the PANOSE system, have been developed for classifying fonts. However, such systems often use a limited amount and type of information that restricts their ability to identify similar fonts. Additionally, the systems are often applied subjectively. As a result of these and other limitations, such systems are often inadequate in sufficiently comparing fonts.